Book Image

Deep Learning with Theano

By : Christopher Bourez
Book Image

Deep Learning with Theano

By: Christopher Bourez

Overview of this book

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.
Table of Contents (22 chapters)
Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


Reinforcement learning describes the tasks of optimizing an agent stumbling into rewards episodically. Online, offline, value-based, or policy-based algorithms have been developed with the help of deep neural networks for various games and simulation environments.

Policy-gradients are a brute-force solution that require the sampling of actions during training and are better suited for small action spaces, although they provide first solutions for continuous search spaces.

Policy-gradients also work to train non-differentiable stochastic layers in a neural net and back propagate gradients through them. For example, when propagation through a model requires to sample following a parameterized submodel, gradients from the top layer can be considered as a reward for the bottom network.

In more complex environments, when there is no obvious reward (for example understanding and inferring possible actions from the objects present in the environment), reasoning helps humans optimize their...